Diffusion MRI data analysis using brain segmentation from anatomical images synthesized from diffusion data by deep learning (DeepAnat)
Ziyu Li1, Qiuyun Fan2,3, Berkin Bilgic2,3, Guangzhi Wang4, Jonathan R Polimeni2,3, Susie Y Huang2,3, and Qiyuan Tian2,3
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, 3Harvard Medical School, Boston, MA, United States, 4Department of Biomedical Engineering, Tsinghua University, Beijing, China
The analysis of diffusion MRI data requires brain segmentation from separate anatomical images, which may be unavailable or cannot be accurately co-registered to diffusion images due to image distortions in diffusion data. Two state-of-the-art convolutional neural networks, U-Net and generative adversarial network (GAN), are employed to synthesize high-quality, distortion-matched T1w images directly from diffusion data, suitable for generating accurate cerebral cortical surfaces and volumetric segmentation for surface-based analysis of DTI metrics and tractography. The accuracy is quantitatively evaluated, and the systematical comparison shows that GAN-synthesized images are more visually appealing while U-Net-synthesized images achieve higher data consistency and segmentation accuracy.
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